74,239 research outputs found

    Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

    Full text link
    Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

    Visualizing practical knowledge: The Haughton-Mars Project

    Get PDF
    To improve how we envision knowledge, we must improve our ability to see knowledge in everyday life. That is, visualization is concerned not only with displaying facts and theories, but also with finding ways to express and relate tacit understanding. Such knowledge, although often referred to as "common," is not necessarily shared and may be distributed socially in choreographies for working together—in the manner that a chef and a maitre d’hôtel, who obviously possess very different skills, coordinate their work. Furthermore, non-verbal concepts cannot in principle be inventoried. Reifying practical knowledge is not a process of converting the implicit into the explicit, but pointing to what we know, showing its manifestations in our everyday life. To this end, I illustrate the study and reification of practical knowledge by examining the activities of a scientific expedition in the Canadian Arctic—a group of scientists preparing for a mission to Mar

    Forecasting and Forecast Combination in Airline Revenue Management Applications

    Get PDF
    Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

    IFSIM Handbook

    Get PDF
    This handbook explains the simulation model IFSIM. IFSIM is an agent based simulation model written in JAVA. The model is constructed for analyzing demographic and economic issues. The aim of the model is to include the main consumption and production patterns over the life-cycle and thus being able to test demo-economic interactions.agent-based modelling; simulation model; JAVA; demogrphy; economy; demo-economic interactions

    Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    Get PDF
    We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

    Complete Issue 4, 1989

    Get PDF

    An Analysis of Logistics Pedagogical Literature: Past and Future Trends in Curriculum, Content, and Pedagogy

    Get PDF
    There presently is no comprehensive review which systematizes and summarizes the burgeoning body of logistics educational literature. The purpose of this paper is to provide a guide for both educators and practitioners to assess the history, current status, and future trends in logistics education in order to nurture advancement in logistics education. This paper draws its conclusions based upon a literature review and categorizes the evolution of logistics education into three areas: defining curriculum, developing content and skills taught, and refining teaching methods. Logistics education continues to benefit from strong ties to industry. Additionally, four principle macro-environmental factors were discovered that impact the current status of logistics education: an increase in the number of logistics educational programs, limited supply of logistics-trained faculty, changes to content requirements, and a changing teaching environment. Future research directions from the published literature are summarized. As current logistics programs continue to evolve and the number of logistics and supply chain management programs continue to increase in response to industry demand, this comprehensive review of the logistics literature may help serve as a benchmark for past and current practices in logistics education. The early partnership between industry and education set the stage to help guide educators to evolve logistics education to address practitioner needs. Increased interest in logistics education and changing environmental factors suggest the need for continued collaboration to further logistics education. The literature demonstrates successful dynamic behavior in response to dynamic industries. It highlights factors which may drive further evolution of logistics education and proposes areas impacted

    Remote sensing information sciences research group

    Get PDF
    Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail
    • …
    corecore